Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (9): 65-73.doi: 10.16180/j.cnki.issn1007-7820.2022.09.010

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Vector Control of Induction Motor Based on Adaptive Fuzzy Neural Network

JIN Aijuan,SHAO Feixuan,YAN Ziguang   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2021-03-05 Online:2022-09-15 Published:2022-09-15
  • Supported by:
    National Natural Science Foundation of China(11502145)


In view of the problem that the induction motor has fixed parameters and is easy to overshoot in the traditional PI control, a method based on the adaptive fuzzy neural network PI control and the full-order adaptive observer is proposed in this study. According to the mathematical model of the induction motor, the model of the full-order adaptive observer is established, and the stability analysis and design are carried out using the Lyapunov stability theory, and the speed adaptive law is deduced. The motor speed outer loop PI is adjusted and optimized online by an adaptive fuzzy neural network inference system. Compared with traditional control schemes, this method is easy to implement, can effectively improve control accuracy, suppress external disturbances, and save sensor costs. MATLAB/Simulink simulation experiments show that the proposed scheme not only improves the dynamic performance of the speed sensorless induction motor vector control system, but also reduces the influence of external load disturbances, and improves the system's adaptability and robustness.

Key words: induction motor, no speed sensor, vector control, PI controller, ANFIS, full-order adaptive observer, online tuning, robustness

CLC Number: 

  • TN787